PROJECT 3: CELL-CELL COMMUNICATION AND HETEROGENEITY SUMMARY Understanding tissue-level organization of biological systems is particularly challenging as tissues are comprised of a heterogeneous collection of cells and the cells within the tissue communicate with each other to coordinate their activities. Approaches that have been successful in other disciplines, such as statistical mechanics, fail to capture the emergent properties of biological tissues. Therefore, there is a need to develop new frameworks to enable deeper understanding the organization of biological tissues. We propose to use the maps-to-models paradigm to study two models of biological tissue organization: (1) antibiotic resistance of a biofilm of Bacillus subtilis; and (2) induction of viral protection through type I interferon response in lung epithelial cells during influenza infection. The implementation of the maps-to- models paradigm will be done through the completion of the following three aims. First, data on spatiotemporal cellular states within tissue will be acquired to construct of maps of the dynamic tissue organization. The maps-to-models paradigm is based on the use of systems-wide datasets as a constraint when building mathematical models. In this aim we will use custom-made microfluidic devices to collect data at single-cell resolution on cellular states within a tissue. Using fluorescent reporters, we will monitor the movement, replication, death, cell state and quorum sensing communications in the biofilm. Similar techniques will be done within an epithelial monolayer during influenza infection to track infection, cellular antiviral response and responses to interferon signals. Next, we will construct a detailed model of the spatiotemporal dynamics within both tissues. By tracking individual cells over time, we will determine the number and pattern of cellular states. We will construct the interaction networks between cells based on a reaction-diffusion framework to assign weights on the relative influence of cells on each other as a function of their distance. The mathematical frameworks that will be used for both systems are very similar. Finally, the models generated above will be used to make specific predictions about the emergent properties of biological tissues. For the biofilm model, predictions will be made about the spatiotemporal dynamics of antibiotic resistance. For the viral infection model, predictions will be made about the specific number of macrophages needed to prevent the spread of viruses. Both predictions are quantitative and therefore will be tested through direct comparison of prediction with experimental results in the two systems. The utilization of two model systems in the above aims will demonstrate the validity and predictive power of the overall maps-to-models approach and will suggest that this paradigm could be successfully applied to gain better quantitative predictive understanding of biological tissue-level organization.